U.S. patent number 6,835,572 [Application Number 09/691,776] was granted by the patent office on 2004-12-28 for magnetic resonance spectroscopy of breast biopsy to determine pathology, vascularization and nodal involvement.
This patent grant is currently assigned to Institute for Magnetic Resonance Research, National Research Council of Canada. Invention is credited to Carolyn E. Mountford, Peter Russell, Ian C. P. Smith, Rajmund L. Somorjai.
United States Patent |
6,835,572 |
Mountford , et al. |
December 28, 2004 |
Magnetic resonance spectroscopy of breast biopsy to determine
pathology, vascularization and nodal involvement
Abstract
Robust classification methods analyse magnetic resonance
spectroscopy (MRS) data (spectra) of fine needle aspirates taken
from breast tumours. The resultant data when compared with the
histopathology and clinical criteria provide computerized
classification-based diagnosis and prognosis with a very high
degree of accuracy and reliability. Diagnostic correlation
performed between the spectra and standard synoptic pathology
findings contain detail regarding the pathology (malignant versus
benign), vascular invasion by the primary cancer and lymph node
involvement of the excised axillary lymph nodes. The classification
strategy consisted of three stages: pre-processing of MR magnitude
spectra to identify optimal spectral regions, cross-validated
Linear Discriminant Analysis, and classification aggregation via
Computerised Consensus Diagnosis. Malignant tissue was
distinguished from benign lesions with an overall accuracy of 93%.
From the same spectrum, lymph node involvement was predicted with
an accuracy of 95% and tumour vascularisation with an overall
accuracy of 92%.
Inventors: |
Mountford; Carolyn E. (East
Ryde, AU), Russell; Peter (Pyme, AU),
Smith; Ian C. P. (Winnipeg, CA), Somorjai; Rajmund
L. (Headingley, CA) |
Assignee: |
Institute for Magnetic Resonance
Research (St. Leonards, AU)
National Research Council of Canada (Ottawa,
CA)
|
Family
ID: |
33518710 |
Appl.
No.: |
09/691,776 |
Filed: |
October 18, 2000 |
Current U.S.
Class: |
436/63; 436/173;
600/410; 702/19 |
Current CPC
Class: |
G01N
24/08 (20130101); G01R 33/4625 (20130101); G01R
33/465 (20130101); A61P 43/00 (20180101); G01R
33/20 (20130101); G16H 10/40 (20180101); Y10T
436/24 (20150115) |
Current International
Class: |
G01R
33/44 (20060101); G01R 33/46 (20060101); G01R
33/465 (20060101); G01N 24/00 (20060101); G01N
24/08 (20060101); G01N 033/48 (); G06F 019/00 ();
A61B 005/05 () |
Field of
Search: |
;436/63,173 ;702/19
;600/410 |
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|
Primary Examiner: Soderquist; Arlen
Attorney, Agent or Firm: Phillips; Peter J. Cooper &
Dunham LLP
Government Interests
STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT
The work described herein was supported by U.S. Army Grant number
DAMD 17-96-1-6077 and NH & MRC 950215 and NH & MRC 973769.
Parent Case Text
CROSS REFERENCES TO RELATED APPLICATIONS
This application claims priority on, and incorporates by reference,
United States Provisional Application Ser. No. 60/160,029 filed
Oct. 18, 1999.
Claims
We claim:
1. A method for obtaining a statistical classifier for classifying
spectral data from a biopsy of breast tissue to determine the
classification of a characteristic of the breast tissue,
comprising: (a) locating a plurality of maximally discriminatory
subregions in magnetic resonance spectra of biopsies of breast
tissue having known classifiers of a characteristic, (b)
cross-validating the spectra by selecting a first portion of the
spectra comprising about one-half of the spectra leaving the other
one-half of the spectra in the remainder of the spectra, developing
linear discriminant analysis classifiers from said first portion of
spectra, and validating the remainder of the spectra using the
classifiers from the first portion of the spectra, to obtain
optimized linear discriminant analysis coefficients, (c) repeating
step (b) a plurality of times, each time selecting a different
portion of the spectra, to obtain a different set of optimized
linear discriminant analysis coefficients for each of said
plurality of times; and (d) obtaining a weighted average of the
linear discriminant analysis coefficients to obtain final
classifier spectra indicating the classification of the
characteristic based on the spectra; wherein spectra from a biopsy
of breast tissue of unknown classification of a characteristic may
be compared to the final classifier spectra to determine the
classification of the characteristic of the breast tissue.
2. The method of claim 1 wherein the step of cross-validating the
spectra comprises cross-validating the spectra by randomly
selecting about half of the spectra.
3. The method of claim 1, further including the step of obtaining a
biopsy of breast tissue by a fine needle aspiration biopsy.
4. The method of claim 1, wherein the step of repeating step (b) a
plurality of times comprises repeating step (b) about 500-1000
times.
5. The method of claim 1, further including the steps of obtaining
a plurality of final classifier spectra independently, and
aggregating the results of the independent classifiers to obtain a
consensus diagnosis.
6. The method according to claim 1, wherein the characteristic is
pathology of the breast tissue and the classification indicates
whether the pathology is malignant, benign or normal.
7. The method according to claim 1, wherein the characteristic is
tumor vascularization, and the classification indicates the extent
of tumor vascularization.
8. The method according to claim 1, wherein the characteristic is
tumor nodal involvement and the classification indicates the extent
of tumor nodal involvement.
9. An apparatus for obtaining a statistical classifier for
classifying spectral data from a biopsy of breast tissue to
determine the classification of a characteristic of the breast
tissue, comprising: (a) a locator for locating a plurality of
maximally discriminatory subregions in magnetic resonance spectra
of biopsies of breast tissue having known classifiers of a
characteristic of breast tissue, (b) a cross-validator for
selecting a first portion of the spectra comprising about one-half
of the spectra leaving the other one-half of the spectra in the
remainder of the spectra, developing linear discriminant analysis
classifiers from said first portion of spectra, and validating the
remainder of the spectra using the classifiers from the first
portion of the spectra, to obtain optimized linear discriminant
analysis coefficients, said cross-validator selecting, developing
and validating a plurality of times, each time selecting a
different portion of the spectra, to obtain a different set of
optimized linear discriminant analysis coefficients for each of
said plurality of times, and (c) an averager for obtaining a
weighted average of the linear discriminant analysis coefficients
to obtain final classifier spectra indicating the classification of
the characteristic based on the spectra, whereby spectra from a
biopsy of breast tissue of unknown classification of a
characteristic may be compared to the final classifier spectra to
determine the classification of the characteristic of the breast
tissue.
10. The apparatus of claim 9 wherein the cross-validator randomly
selects about half of the spectra.
11. The apparatus of claim 9, wherein the cross-validator repeats
step (b) about 500-1000 times.
12. The apparatus of claim 9, wherein the characteristic is
pathology of the breast tissue and the classification indicates
whether the pathology is malignant, benign or normal.
13. The apparatus of claim 9, wherein the characteristic is tumor
vascularization, and the classification indicates the extent of
tumor vascularization.
14. The apparatus claim 9, wherein the characteristic is tumor
nodal involvement and the classification indicates the extent of
tumor nodal involvement.
15. A method for determining the classification of a characteristic
of breast tissue, comprising: obtaining magnetic resonance spectra
of a biopsy of breast tissue having unknown classification of a
characteristic and comparing the spectra with a classifier, said
classifier having been obtained by: (a) locating a plurality of
maximally discriminatory subregions in the magnetic resonance
spectra of biopsies of breast tissue having known classifications
of a characteristic of the breast tissue, (b) cross-validating the
spectra of (a) by selecting a first portion of spectra comprising
about one-half of the spectra leaving the other one-half of the
spectra in the remainder of the spectra, developing linear
discriminant analysis classifier from said first portion of
spectra, and validating the remainder of the spectra using the
classifications from the first portion of the spectra, to obtain
optimized linear discriminant analysis coefficients, (c) repeating
step (b) a plurality of times, each time selecting a different
portion of the spectra, to obtain a different set of optimized
linear discriminant analysis coefficients for each of said
plurality of times, and (d) obtaining a weighted average of the
linear discriminant analysis coefficients to obtain final
classifier spectra indicating the classification of the
characteristic based on the spectra, and comparing the spectra from
the biopsy of breast tissue having unknown classification to the
final classifier spectra to determine the classification of the
characteristic of the breast tissue.
16. The method of claim 15, wherein the characteristic is pathology
of the breast tissue and the classification indicates whether the
pathology is malignant, benign or normal.
17. The method of claim 15, wherein the characteristic is tumor
vascularization, and the classification indicates the extent of
tumor vascularization.
18. The method of claim 15, wherein the characteristic is tumor
nodal involvement and the classification indicates the extent of
tumor nodal involvement.
19. An apparatus for determining the classification of a
characteristic of breast tissue, comprising: a spectrometer for
obtaining magnetic resonance spectra of a biopsy of breast tissue
having unknown classification of a characteristic; a classifier for
statistically classifying the spectra by comparing the spectra with
a reference classifications, said classifier having been obtained
by: (a) locating a plurality of maximally discriminatory subregions
in the magnetic resonance spectra of biopsies of breast tissue
having known classifications of a characteristic of the breast
tissue, (b) cross-validating the spectra of (a) by selecting a
first portion of spectra comprising about one-half of the spectra
leaving the other one-half of the spectra in the remainder of the
spectra, developing linear discriminant analysis classifier from
said first portion of spectra, and validating the remainder of the
spectra using the classifiers from the first portion of the
spectra, to obtain optimized linear discriminant analysis
coefficients, (c) repeating step (b) a plurality of times, each
time selecting a different portion of the spectra, to obtain a
different set of optimized linear discriminant analysis
coefficients for each of said plurality of times, and (d) obtaining
a weighted average of the linear discriminant analysis coefficients
to obtain final classifier spectra indicating the classification of
the characteristic based on the spectra, and wherein said
classifier compares the spectra from the biopsy of breast tissue
having unknown classification to the final classifier spectra to
determine the classification of the characteristic of the breast
tissue.
20. The apparatus according to claim 19, wherein the characteristic
is pathology of the breast tissue and the classification indicates
whether the pathology is malignant, benign or normal.
21. The apparatus according to claim 19, wherein the characteristic
is tumor vascularization, and the classification indicates the
extent of tumor vascularization.
22. The apparatus according to claim 19, wherein the characteristic
is tumor nodal involvement and the classification indicates the
extent of tumor nodal involvement.
23. A method for obtaining a statistical classifier for classifying
spectral data from a biopsy of tissue to determine the
classification of a characteristic of the tissue, comprising: (a)
locating a plurality of discriminatory subregions in magnetic
resonance spectra of biopsies of tissue having known classifiers of
a characteristic, (b) cross-validating the spectra by selecting a
first portion of the spectra comprising about one-half of the
spectra leaving the other one-half of the spectra in the remaining
portion of the spectra, developing linear discriminant analysis
classifiers from said first portion of spectra, and validating the
remainder of the spectra using the classifiers from the first
portion of the spectra, to obtain optimized linear discriminant
analysis coefficients, (c) repeating step (b) a plurality of times,
each time selecting a different portion of the spectra, to obtain a
different set of optimized linear discriminant analysis
coefficients for each of said plurality of times; and (d) obtaining
a weighted average of the linear discriminant analysis coefficients
to obtain final classifier spectra indicating the classification of
the characteristic based on the spectra, wherein spectra from a
biopsy of tissue of unknown classification of a characteristic may
be compared to the final classifier spectra to determine the
classification of the characteristic of the tissue.
24. An apparatus for obtaining a statistical classifier for
classifying spectral data from a biopsy of tissue to determine the
classification of a characteristic of the tissue, comprising: (a) a
locator for locating a plurality of discriminatory subregions in
magnetic resonance spectra of biopsies of tissue having known
classifiers of a characteristic of tissue, (b) a cross-validator
for selecting a first portion of the spectra comprising about
one-half of the spectra leaving the other one-half of the spectra
in the remaining portion of the spectra, developing linear
discriminant analysis classifiers from said first portion of
spectra, and validating the remainder of the spectra using the
classifiers from the first portion of the spectra, to obtain
optimized linear discriminant analysis coefficients, said
cross-validator selecting, developing and validating a plurality of
times, each time selecting a different portion of the spectra, to
obtain a different set of optimized linear discriminant analysis
coefficients for each of said plurality of times, and (c) an
averager for obtaining a weighted average of the linear
discriminant analysis coefficients to obtain final classifier
spectra indicating the classification of the characteristic based
on the spectra, whereby spectra from a biopsy of tissue of unknown
classification of a characteristic may be compared to the final
classifier spectra to determine the classification of the
characteristic of the tissue.
25. A method for determining the classification of a characteristic
of tissue, comprising: obtaining magnetic resonance spectra of a
biopsy of tissue having unknown classification of a characteristic
and comparing the spectra with a classifier, said classifier having
been obtained by: (a) locating a plurality of discriminatory
subregions in the magnetic resonance spectra of biopsies of tissue
having known classifications of a characteristic of the tissue, (b)
cross-validating the spectra of (a) by selecting a first portion of
spectra comprising about one-half of the spectra leaving the other
one-half of the spectra in the remainder of the spectra, developing
linear discriminant analysis classifier from said first portion of
spectra, and validating the remainder of the spectra using the
classifications from the first portion of the spectra, to obtain
optimized linear discriminant analysis coefficients, (c) repeating
step (b) a plurality of times, each time selecting a different
portion of the spectra, to obtain a different set of optimized
linear discriminant analysis coefficients for each of said
plurality of times, and (d) obtaining a weighted average of the
linear discriminant analysis coefficients to obtain final
classifier spectra indicating the classification of the
characteristic based on the spectra, wherein the spectra from the
biopsy of tissue having unknown classification may be compared to
the final classifier spectra to determine the classification of the
characteristic of the tissue.
26. An apparatus for determining the classification of a
characteristic of tissue, comprising: a spectrometer for obtaining
magnetic resonance spectra of a biopsy of tissue having unknown
classification of a characteristic; a classifier for statistically
classifying the spectra by comparing the spectra with a reference
classifications, said classifier having been obtained by: (a)
locating a plurality of maximally discriminatory subregions in the
magnetic resonance spectra of biopsies of tissue having known
classifications of a characteristic of the tissue, (b)
cross-validating the spectra of (a) by selecting a first portion of
spectra comprising about one-half of the spectra leaving the other
one-half of the spectra in the remainder of the spectra, developing
linear discriminant analysis classifier from said first portion of
spectra, and validating the remainder of the spectra using the
classifiers from the first portion of the spectra, to obtain
optimized linear discriminant analysis coefficients, (c) repeating
step (b) a plurality of times, each time select ing a different
portion of the spectra, to obtain a different set of optimized
linear discriminant analysis coefficients for each of said
plurality of times, and (d) obtaining a weighted average of the
linear discriminant analysis coefficients to obtain final
classifier spectra indicating the classification of the
characteristic based on the spectra, and wherein said classifier
compares the spectra from the biopsy of tissue having unknown
classification to the final classifier spectra to determine the
classification of the characteristic of the tissue.
Description
BACKGROUND OF THE INVENTION
1. Technical Field of the Invention
The present invention relates to the use of magnetic resonance
spectroscopy, and more particularly to such use for determining
pathology, vascularization and nodel involvement of a biopsy of
breast tissue.
2. Description of the Related Art
Within this application several publications are referenced by
arabic numerals within parentheses. Full citations for these and
other references may be found at the end of the specification
immediately preceding the claims. The disclosures of all of these
publications in their entireties are hereby incorporated by
reference into this application in order to more fully describe the
state of the art to which this invention pertains. Clinical
evaluation, mammography and aspiration cytology or core biopsy
(triple assessment) is undertaken on women presenting with breast
lesions in most Western countries. Clinical assessment of palpable
breast lumps is unreliable (1, 2). Impalpable lesions are usually
discovered by screening or diagnostic mammnography, which has a
reported sensitivity of 77-94% and a specificity of 92-95% (3).
Cytological assessment of fine needle aspiration biopsies (FNAB)
has sensitivities ranging from 65-98% and specificities ranging
from 34-100% (4) depending on the skill of the person performing
the aspiration and the expertise of the cytopathologist.
Following surgical excision of the lesion a time consuming process
of preparation and pathological assessment of the specimen
determines the nature of the tumour and the prognostic features
associated with it.
SUMMARY OF THE INVENTION
Magnetic resonance spectroscopy (MRS) is a modality with a proven
record in the diagnosis of minimally invasive malignant lesions
(5-11). MR spectra of small samples of tissue or even cell
suspensions enable the reliable determination of whether the tissue
of origin is malignant or benign. Often MRS is able to detect
malignancy before morphological manifestations are visible by light
microscopy (8).
The potential of proton MRS from FNAB specimens to distinguish
benign from malignant breast lesions has been demonstrated
previously (12). At that time the MRS method relied on visual
reading to process spectra and calculate the ratio of the
diagnostic metabolites choline and creatine. This spectral ratio
allowed tissue to be identified as either benign or malignant. In a
small cohort of 20 patients within that study it also distinguished
high grade ductal carcinoma in situ (DCIS) with comedonecrosis or
microinvasion from low grade DCIS. Despite the limitation of visual
inspection, which could only assess those spectra with a signal to
noise ratio (SNR) of greater than 10, the visual method resulted in
a diagnosis of malignant or benign with a sensitivity and
specificity of 95 and 96%. FIG. 1 shows malignant and benign
spectra with good SNR while FIG. 2 shows spectra with poor SNR.
Twenty percent of the spectra were discarded because low aspirate
cellularity yielded inadequate SNR. In the initial study visual
analysis used only two of fifty or more available resonances (6).
Thus, potentially diagnostic and prognostic information in the
remaining spectrum may have been ignored.
A 3-stage, robust statistical classification strategy (SCS) has
been developed to classify biomedical data and to assess the full
MR spectrum obtained from biological samples. The robustness of the
method has been demonstrated previously with the analysis of proton
MR spectra of thyroid tumours (13), ovarian (14), prostate (9), and
brain tissues (15). The present invention applies SCS to assess
proton MR spectra of breast aspirates against pathological criteria
in order to determine the correct pathology on samples with
sub-optimal cellularity and SNR and to determine if other
diagnostic and prognostic information is available in the
spectra.
The inventors have determined that SCS on MRS from breast FNAB is
more reliable than visual inspection to determine whether a lesion
is benign or malignant, and that a greater proportion of spectra is
useful for analysis. Furthermore, spectral information obtained
from MRS on FNAB of breast cancer specimens predicted lymph node
metastases (overall accuracy of 96%) and vascular invasion (overall
accuracy of 92%).
The invention provides a method for obtaining a statistical
classifier for classifying spectral data from a biopsy of breast
tissue to determine the classification of a characteristic of the
breast tissue, comprising: (a) locating a plurality of maximally
discriminatory subregions in magnetic resonance spectra of biopsies
of breast tissue having known classifiers of a characteristic, (b)
cross-validating the spectra by selecting a portion of the spectra,
developing linear discriminant analysis classifiers from said first
portion of spectra, and validating the remainder of the spectra
using the classifiers from the first portion of the spectra, to
obtain optimized linear discriminant analysis coefficients, (c)
repeating step (b) a plurality of times, each time selecting a
different portion of the spectra, to obtain a different set of
optimized linear discriminant analysis coefficients for each of
said plurality of times; (d) obtaining a weighted average of the
linear discriminant analysis coefficients to obtain final
classifier spectra indicating the classification of the
characteristic based on the spectra; and (e) comparing spectra from
a biopsy of breast tissue of unknown classification of a
characteristic to the final classifier spectra to determine the
classification of the characteristic of the breast tissue.
The invention provides an apparatus for obtaining a statistical
classifier for classifying spectral data from a biopsy of breast
tissue to determine the classification of a characteristic of the
breast tissue, comprising: (a) a locator for locating a plurality
of maximally discriminatory subregions in magnetic resonance
spectra of biopsies of breast tissue having known classifiers of a
characteristic of breast tissue, (b) a cross-validator for
selecting a portion of the spectra, developing linear discriminant
analysis classifiers from said first portion of spectra, and
validating the remainder of the spectra using the classifiers from
the first portion of the spectra, to obtain optimized linear
discriminant analysis coefficients, said cross-validator selecting,
developing and validating a plurality of times, each time selecting
a different portion of the spectra, to obtain a different set of
optimized linear discriminant analysis coefficients for each of
said plurality of times, and (c) an averager for obtaining a
weighted average of the linear discriminant analysis coefficients
to obtain final classifier spectra indicating the classification of
the characteristic based on the spectra,
whereby spectra from a biopsy of breast tissue of unknown
classification of a characteristic may be compared to the final
classifier spectra to determine the classification of the
characteristic of the breast tissue.
The invention provides a method for determining the classification
of a characteristic of breast tissue, comprising: obtaining
magnetic resonance spectra of a biopsy of breast tissue having
unknown classification of a characteristic and comparing the
spectra with a classifier, said classifier having been obtained by:
(a) locating a plurality of maximally discriminatory subregions in
the magnetic resonance spectra of biopsies of breast tissue having
known classifications of a characteristic of the breast tissue, (b)
cross-validating the spectra of (a) by selecting a portion of
spectra, developing linear discriminant analysis classifier from
said first portion of spectra, and validating the remainder of the
spectra using the classifications from the first portion of the
spectra, to obtain optimized linear discriminant analysis
coefficients, (c) repeating step (b) a plurality of times, each
time selecting a different portion of the spectra, to obtain a
different set of optimized linear discriminant analysis
coefficients for each of said plurality of times, and (d) obtaining
a weighted average of the linear discriminant analysis coefficients
to obtain final classifier spectra indicating the classification of
the characteristic based on the spectra, and
comparing the spectra from the biopsy of breast tissue having
unknown classification to the final classifier spectra to determine
the classification of the characteristic of the breast tissue.
The invention provides an apparatus for determining the
classification of a characteristic of breast tissue, comprising: a
spectrometer for obtaining magnetic resonance spectra of a biopsy
of breast tissue having unknown classification of a characteristic;
a classifier for statistically classifying the spectra by comparing
the spectra with a reference classifications, said classifier
having been obtained by: (a) locating a plurality of maximally
discriminatory subregions in the magnetic resonance spectra of
biopsies of breast tissue having known classifications of a
characteristic of the breast tissue, (b) cross-validating the
spectra of (a) by selecting a portion of spectra, developing linear
discriminant analysis classifier from said first portion of
spectra, and validating the remainder of the spectra using the
classifiers from the first portion of the spectra, to obtain
optimized linear discriminant analysis coefficients, (c) repeating
step (b) a plurality of times, each time selecting a different
portion of the spectra, to obtain a different set of optimized
linear discriminant analysis coefficients for each of said
plurality of times, and (d) obtaining a weighted average of the
linear discriminant analysis coefficients to obtain final
classifier spectra indicating the classification of the
characteristic based on the spectra, and
wherein said classifier compares the spectra from the biopsy of
breast tissue having unknown classification to the final classifier
spectra to determine the classification of the characteristic of
the breast tissue.
DESCRIPTION OF THE DRAWINGS
FIG. 1 shows malignant and benign spectra with relatively good
SNR;
FIG. 2 shows spectra with relatively poor SNR; and
FIG. 3 shows a system for determining pathology, vascularization
and nodal involvement according to the invention.
DETAILED DESCRIPTION OF THE INVENTION
Methods
Preparation of Patients:
Intra-operative FNAB were taken from 139 patients undergoing breast
surgery for malignant and benign conditions (Table 1) by three
surgeons in separate hospitals. In order to provide a sufficiently
large data set for SCS an additional 27 patients joined the study
(see Table 1). Impalpable breast lesions that had been localised by
carbon track or hook wire were included except if the lesion was
not palpable at excision or when the pathology specimen could have
been compromised. All samples were taken during surgery under
direct vision after the lesion had been identified and incised
sufficiently widely to ensure that the FNAB and tissue specimens
represented the same lesion and were thus comparable. The lesion
was identified and incised in-vivo via the margin with the greatest
apparent depth of normal tissue between it and the lesion to ensure
the pathologist could report upon the lesion according to a
standard protocol. Malignant and suspicious lesions were orientated
with sutures and radio opaque vascular clips (Ligaclips) for
pathological and radiological orientation. The FNAB was collected
by the surgeon using a 23-gauge needle on a 5 ml syringe. The
number of needle passes was recorded and the surgeon's evaluation
of the quality of the aspirate was made. Before the needle was
removed from the lesion, a tissue sample including the relevant
part of the needle track was taken. The size of this tissue
specimen was estimated and recorded by the surgeon.
Pre-operative clinical and investigative data included localised
pain, nipple discharge or nipple crusting, details of previous
mammography, and whether the lesion was detected through screening.
The clinical, mammography, ultra sonographic, cytological, core
biopsy and MRI details were recorded as malignant, suspicious,
benign, impalpable, uncertain or not done.
The pathology specimen was sent on ice at the initial stages, but
later in formalin, for standard histopathological reporting and
hormone receptor analysis. The pathology report was issued in
synoptic format (16).
Specific tumour-related clinical and sampling information was
collected. These included a history of previous breast biopsies
with dates, diagnoses, sizes and sites of these lesions along with
the current lesion's duration, palpability, laterality, size and
locality within the breast. The date of operation, the extent of
breast surgery from open biopsy to total mastectomy, and axillary
surgery from sampling to level 3 dissection was recorded.
Specimen Preparation:
Following complete excision of the lesions the FNAB cytology and
tissue specimens were placed in polypropylene vials containing 300
ml phosphate-buffered saline (PBS) in D.sub.2 O. All specimens were
immediately immersed in liquid nitrogen and stored at -70.degree.
C. for up to 6 weeks until MRS analysis. Prior to the proton MRS
experiment, each FNAB specimen was thawed and transferred directly
to a 5 mm MRS tube. The volume was adjusted to 300 ml with
PBS/D.sub.2 O where necessary. Proton MRS assessment of all
specimens was performed without knowledge of the correlative
histopathology, either from the synoptic pathology report or from
sectioning of tissue used in MRS study.
The sample of tissue excised around the needle tract was similarly
placed in polypropylene vials containing 300 ml PBS/D.sub.2 O and
immersed in liquid nitrogen as described above. This sample was
later used for pathological correlation.
Data Acquisition:
MRS experiments were carried out on a Bruker Advance 360 wide-bore
spectrometer (operating at 8.5 Tesla) equipped with a standard 5 mm
dedicated proton probe head. The sample was spun at 20 Hz and the
temperature maintained at 37.degree. C. The residual water signal
was suppressed by selective gated irradiation. The chemical shifts
of resonances were referenced to aqueous sodium
3-(trimethylsilyl)-propanesulphonate (TSPS) at 0.00 ppm.
One-dimensional spectra were acquired over a spectral width of 3597
Hz (10.0 ppm) using a 90.degree. pulse of 6.5-7 .mu.s, 8192 data
points, 256 accumulations and a relaxation delay of 2.00 seconds,
resulting in a pulse repetition time of 3.14 seconds.
SNR was determined using the Bruker standard software (xwinnmr).
The noise region was defined between 8.5 to 9.5 ppm. The signal
region was defined between 2.8 to 3.5 ppm.
Histopatholos:
Diagnostic correlation was obtained by comparing spectral analysis
with the hospital pathology report provided for each patient. Lymph
node involvement and vascular invasion were determined from the
reports only in cases where this information was complete. In the
participating hospitals lymph nodes were embedded and serial
sectioned in standard fashion. One 5 .mu.m section out of every 50
(i.e., each 250 .mu.m) was stained and examined. All intervening
sections were discarded.
In the initial phase of the study, cytological analysis of the
aspirate after MRS analysis was attempted but cellular detail was
compromised by autolytic changes and this approach was not pursued.
In order to verify FNAB sampling accuracy, a separate
histopathological assessment by a single pathologist (PR) was
obtained from tissue removed from the aspiration site of the MRS
sample. Tissue specimens were thawed, fixed in FAA (formalin/acetic
acid/alcohol), paraffin-embedded, sectioned at 7 .mu.m, stained
with haematoxylin and eosin according to standard protocols and
reviewed under the light microscope by the pathologist without
access to the clinical or MRS data. Tissue preservation, abundance
of epithelial cells relative to stroma, and presence of potentially
confounding factors such as fat and inflammatory cells were
reported in addition to the principal diagnosis.
Statistical Classification Strategy:
The general classification strategy has been developed and was
designed specifically for MR and IR spectra of biofluids and
biopsies. The strategy consists of three stages. First the MR
magnitude spectra are preprocessed, (in order to eliminate
redundant information and/or noise) by submitting them to a
powerful genetic algorithm-based Optimal Region Selection (ORS_GA)
(17), which finds a few (at most 5-10) maximally discriminatory
subregions in the spectra. The spectral averages in these
subregions are the ultimate features and used at the second stage.
This stage uses the features found by ORS_GA to develop Linear
Discriminant Analysis (LDA) classifiers that are made robust by
IBD's bootstrap-based cross-validation method (18). The
crossvalidation approach proceeds by randomly selecting about half
the spectra from each class and using these to train a classifier
(usually LDA). The resulting classifier is then used to validate
the remaining half This process is repeated B times (with random
replacement), and the optimized LDA coefficients are saved. The
weighted average of these B sets of coefficients produces the final
classifier. The ultimate classifier is the weighted output of the
500-1000 different bootstrap classifier coefficient sets and was
designed to be used in a clinical setting as the single best
classifier. The classifier consists of probabilities of class
assignment for the individual spectra. For 2-class problems, class
assignment is called crisp if the class probability is >0.75%.
For particularly difficult classification problems the third stage
is activated. This aggregates the outputs (class probabilities) of
several independent classifiers to form a Computerised Consensus
Diagnosis (CCD) (13, 15). The consequence of CCD is that
classification accuracy and reliability is generally better than
the best of the individual classifiers.
FIG. 3 shows a spectrometer 10, which may be a Bruker Advance 360
spectrometer operating at 8.5 Tesla, with equipped computer. The
statistical classification strategy (SCS) computer 12 stores the
SCS and other programs described herein. The clinical data base
includes the information from the data acquisition and
histopathology, used by the computer 12 to develop the classifier
16. The classifier 16 classifies the characteristics (e.g.
pathology, vascularization and/or lymph node involvement) of the
breast tissue under examination.
Results
One hundred and sixty-six patients were involved in the study. A
summary of the clinicopathological criteria is shown in Table
1.
Benign versus Malignant:
Proton MR spectra were recorded for each FNAB irrespective of the
cellularity of the aspirate. However, those specimens with a SNR
less than 10, which were shown to be inadequate for visual
inspection (12) have been included in the SCS analysis without
significantly compromising accuracy. Visual inspection of all
spectra irrespective of signal to noise gave a sensitivity and
specificity of 85.3% and 81.5% respectively (Table 2a), based on
the creatine-to-choline ratio. When SCS-based classifiers were
developed for all available spectra (Table 2b), 96% of the spectra
were considered crisp and could be assigned unambiguously by the
classifier as malignant or benign. Sensitivity and specificity were
93% and 92% respectively.
After removing the 31 spectra with the previously determined poor
SNR (SNR<10), a sensitivity and specificity of 98% and 94%,
respectively, was achieved with crispness of 990/% (Table 2c).
Prognostic Factors:
With the addition of prognostic criteria to the database two
further classifiers were created, namely, lymph node involvement
and vascular invasion. A small number of known benign or
pre-invasive cases were included in these subsets to assess the
computer's ability to correctly define those cases in which no
nodal involvement or vascular invasion was expected. These benign
or pre-invasive cases were all correctly assigned by the computer
into their respective uninvolved classes.
Lymph Node Involvement:
There were 31 cases with nodal involvement and 30 without including
2 DCIS and 3 fibrocystic specimens. All spectra were included
irrespective of SNR. Only those spectra for which complete
pathology and clinical reports were available were included in this
comparison (Table 1). The presence of lymph node metastases was
predicted by SCS with a sensitivity of 96% and specificity of 94%
(Table 3a).
Vascular Invasion:
SCS-based analysis of spectra was also carried out using vascular
invasion as the criterion. There were 85 spectra for this analysis
(Table 1). A sensitivity of 84% and specificity of 100% was
achieved for the correct determination of vascular invasion, with
an overall accuracy of 92% (Table 3b).
Discussion
The introduction of preprocessing and SCS analysis of MR spectra
has enhanced the ability to correlate spectroscopic changes with
the pathology of human biopsies. It has also allowed specimens with
sub-optimal cellularity to be analysed, and more importantly,
provided a correlation with clinical criteria not apparent by
visual inspection.
Visual inspection of spectra, like histopathology, is limited by
the experience and skill of the reader for determining peak height
ratios of metabolites (12). Visual inspection of spectra and the
use of peak height ratio measurements of choline and creatine
discriminated benign from malignant spectra with a higher degree of
accuracy than standard triple assessment of breast lesions.
However, to attain a high degree of accuracy, many spectra with
poor SNR had to be discarded, reducing the effectiveness of the
technique. Previous estimates of cellular material derived from
FNAB, on which to perform MRS analysis reliably, have suggested
that at least 10.sup.6 cells are needed (6).
By using SCS-derived classifiers it was possible to distinguish
malignant from benign pathologies with higher sensitivity 92% and
specificity 96% for all FNAB spectra including those with low SNR
(Table 2b) than by visual reading of these same spectra (Table 2a).
That SCS-based analysis could more reliably classify a greater
proportion of spectra than could be visually assessed is testament
to the robustness and greater generality of the computer-based
approach.
The SCS-based result is further improved by presenting to the
computer spectral data with high SNR. The improvement in
sensitivity and specificity gained for spectra with SNR>10
(Table 2c) illustrates this point. Obtaining FNAB with adequate
cell numbers can also enhance the results.
SCS permits classifiers to recognize patterns containing more
complex information. The classifier has been validated to diagnose
specimens with lymph node involvement and vascular invasion. The
ability of the SCS-derived classifier to predict lymph node
involvement with an accuracy of 95% and vascular invasion with an
accuracy of 92% emphasises the wealth of chemical information that
can be extracted, with the appropriate statistical approach, from
an FNAB of a breast lesion (Table 3).
A major challenge in breast cancer is the need to identify and
understand the factors that most influence the patient's prognosis
and through timely and appropriate intervention influence this
outcome. Adjuvant therapy can reduce the odds of death during the
first ten years after diagnosis of breast cancer by about of 20-30%
(19). The best prognostic indicator of survival in patients with
early breast cancer has been shown to be axillary lymph node status
(20-22).
Increasingly, sentinel lymph node biopsy is being investigated as a
means to reduce the morbidity and cost of unnecessary axillary
dissection in the two thirds of women with early invasive breast
cancer who prove to be node-negative (23-25), while preserving the
option of full axillary node clearance in those patients who are
node-positive. MRS may possibly determine nodal involvement from
the cellular material derived solely from the primary tumour, thus
limiting the role of sentinel lymph node biopsy.
The results, that 52% of patients with lymph node involvement also
had vascular invasion, is in agreement with Barth et al (26), who
showed that peritumoural lympho-vascular invasion correlated with
lymph node involvement (27) and was an independent predictor of
disease free and overall survival (28-31).
A computer-based statistical classification strategy providing a
robust means of analysing clinical data is becoming a reality. The
power, speed and reproducibility of a computer-based diagnosis may
lead to suitably programmed computers supplanting the human
observer in the clinical laboratory. Patients increasingly expect
certainty in diagnosis and optimum management.
Several important experimental factors should be noted. Presently,
the MRS method according to the invention has thus far been
demonstrated to work only on aspirated cells from the breast and
not on core biopsies that contain a sufficiently high level of fat
to mask diagnostic and prognostic information. The biopsy should be
representative of the lesion and contain sufficient cellularity.
Furthermore, sample handling is of paramount importance if the
specimen is to be minimally degraded. Quality control in the
spectrometer should be exercised with regard to pulse sequences,
temperature, magnet stability, shimming and water suppression. The
magnetic field at which the database reported herein was collected
is 8.5 Tesla (360 MHz for proton). Because spectral patterns are
frequency dependent, a new classifier should be developed if one
uses different magnetic field strengths.
The clinical and pathology databases used to train the classifier
should be representative of the full range of pathologies or the
complete demographics of the population, or else the classifier may
be inadequately prepared for all the possibilities it might
encounter in clinical practice. In developing a database for breast
lesions, the training set should have adequate samples of all the
commonly encountered breast pathologies and be updated upon
detection of less common tumour types.
The invention is expected to provide a revolutionary impact on
breast cancer management by the use of SCS computerised analysis of
MR spectral features, by obtaining a much higher level of accuracy
in diagnosis of the lesion and also an indication of its metastatic
potential when compared to visual inspection of spectra Most
importantly, the invention facilitates identification of the stage
of the disease from spectral information of FNAB collected only
from the primary breast lesion.
The invention allows one to determine pathological diagnosis, the
likelihood of axillary lymph nodal involvement and tumour
vascularisation by SCS-based analysis of proton MR spectra of a
FNAB taken from a primary breast lesion. The SCS-based method is
more accurate and reliable than visual inspection for identifying
complex spectral indicators of diagnosis and prognosis.
The ability of an SCS-based analysis of MRS data to provide
prognostic information on lymph node involvement by sampling only
the primary tumour may provide a paradigm shift in the management
of breast cancer. The determination of vascular invasion from the
same cellular material highlights the untapped potential of MRS to
determine prognostic information.
Although one embodiment of the invention has been shown and
described, numerous variations and modifications will readily occur
to those skilled in the art. The invention is not limited to the
preferred embodiment, and its scope is determined only by the
appended claims.
TABLE 1 Summary of Clinico-pathological Data All patients
Benign/Malignant Lymph Nodes Vascular Invasion (n = 66) (n = 140)
(n = 61) (n = 85) Age Mean .+-. SD (Range) 55.8 .+-. 15.4 (20-101)
54.7 .+-. 15 (20-90) 58.4 .+-. 13.2 (29-85) 60.6 .+-. 14.3 (29-101)
Pathology type Invasive Ductal 89 74 52 68 Invasive Lobular 8 8 3 5
Mixed Ductal/Lob. 1 1 1 DCIS 10 1 2* 9* Fibroadenoma 17 17
Fibrocystic 22 22 3* 2* Papilloma 3 2 Radial Scar 2 2 Gynaecomastia
1 1 Misc. Benign 13 12 1 Total 166 140 61 85 *These preinvasive and
benign lesions were included as known lymph node negative, vascular
invasion negative cases to test the computer's ability to discern
true negatives and positives. They were all correctly classified by
the computer into their respective classes.
TABLE 2 Maglignant versus Benign a. Visual Inspection: Malignant
versus Benign Sensitivity Specificity For all Spectra Malignant (n
= 83) vs Benign (n = 57) 85.3% 81.5% Spectra SNR > 10 Malignant
(n = 60) vs Benign (n = 49) 100% 87.3% b. SCS: Malignant or Benign
(All spectra): (M:83, B:57) B M Sensitivity Specificity PPV % Crisp
B 51 4 92.7% 92.4% 92.4% 96.5% M 6 73 92.4% 92.7% 92.7% 95.2%
Overall Accuracy: 92.6% Overall % Crisp: 95.7% (134 of 140) x =
0.922 c. SCS: Malignant or Benign (SNR > 10): (M:60, B:49) B M
Sensitivity Specificity PPV % Crisp B 46 3 93.9% 98.3% 98.2% 100.0%
M 1 58 98.3% 93.9% 94.1% 98.3% Overall Accuracy: 96.1% Overall %
Crisp: 99.1% (108 of 109) x = 0.922
TABLE 3 SCS:-Prognostic Indicators a. Lymph Node involvement: (P:
(Present) 29, A: (Absent) 32) P A Sensitivity Specificity PPV %
Crisp P 25 1 96.2% 93.8% 93.9% 89.7% A 2 30 93.8% 96.2% 96.1% 100%
Overall Accuracy: 95.0% Overall % crisp: 95.1% (58 of 61) x = 0.899
b. Vascular Invasion: (P: (Present) 33, A: (Absent) 52) P A
Sensitivity Specificity PPV % Crisp P 26 5 83.9% 100.0% 100.0%
93.9% A 0 49 100.0% 83.9% 86.1% 94.2% Overall Accuracy: 91.9%
Overall % crisp: 94.1% (80 of 85) x = 0.839
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